PV temporal output shaping - visualization¶

Instructions¶

Make sure to update the revmpath, datapath, apikeys, and nsrdbparams variables in settings.py. This notebook makes extensive use of the plots.py module within pvvm.

### Standard imports
import pandas as pd
import numpy as np
import os, sys, site, pickle, json, shapely, zipfile
import matplotlib.pyplot as plt
import matplotlib as mpl
from glob import glob
from tqdm import tqdm, trange, tqdm_notebook, tnrange
import urllib, requests
import geopandas as gpd
import pvlib
from IPython.display import display, HTML

from matplotlib.ticker import (
    AutoMinorLocator, MultipleLocator, AutoLocator, PercentFormatter)
pd.options.display.max_columns = 200
pd.options.display.max_rows = 15
%config InlineBackend.figure_format = 'retina'

### Import the package
### (Note: scriptpath should lead to folder above 'pvvm' folder)
# scriptpath = os.path.expanduser('~/path/to/folder/above/pvvm/')
scriptpath = os.path.expanduser('~/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/')
site.addsitedir(scriptpath)
import pvvm

revmpath = pvvm.settings.revmpath
datapath = pvvm.settings.datapath
pvvm.plots.plotparams()

Import results¶

### Load TMY-optimized orientations
dfin = pd.read_csv(revmpath+'out/results_pvtos_tmyopt_public.csv.gz')
for col in ['ISOwecc','ISO:Node']:
    dfin[col] = dfin[col].astype('category')
dfin['yearsun'] = dfin['yearsun'].map(lambda x: 'tmy' if x == 'tmy' else int(float(x))).copy()
### Drop the Canada nodes
dfin = dfin.loc[~(dfin['ISO:Node'].isin(['MISO:MHEB','MISO:SPC']))].reset_index(drop=True)
    
### Load final revenue and CF results calculated under historical irradiance
dfin_optorient_histsun = pd.read_csv(revmpath+'out/results_pvtos_histsun_public.csv.gz')
for col in ['ISOwecc','ISO:Node']:
    dfin_optorient_histsun[col] = dfin_optorient_histsun[col].astype('category')
dfin_optorient_histsun['yearsun'] = dfin_optorient_histsun['yearsun'].map(
    lambda x: 'tmy' if x == 'tmy' else int(float(x))).copy()
### Drop the Canada nodes
dfin_optorient_histsun = dfin_optorient_histsun.loc[
    ~(dfin_optorient_histsun['ISO:Node'].isin(['MISO:MHEB','MISO:SPC']))].reset_index(drop=True)

/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (12,38,39,41,45,46,51,55,56,57,61) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (2,3,6,31) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

Shared data and figure properties¶

isos = ['CAISO','ERCOT','MISO','PJM','NYISO','ISONE']
colors = dict(zip(isos, ['C{}'.format(i) for i in range(len(isos))]))
alpha = 0.2
years = list(range(2010,2018))
mc = {'tmy':'k','da':'C0', 'rt':'C3'}
bootstrap = 5 ### Use 5 for testing and 5000 for publication
figheight = 12

innerband = ['25%','75%']
outerband = ['2.5%','97.5%']
percentiles = innerband + ['50%'] + outerband
fractions = [float(pct[:pct.find('%')])*.01 for pct in percentiles]
labels = {
    'outer': 'central {:.0f}%'.format((fractions[-1]-fractions[-2])*100),
    'inner': 'central {:.0f}%'.format((fractions[1]-fractions[0])*100),
    'median': 'median',
    'min': 'min & max',
    'max': '_nolabel_',
    'minmax': 'all nodes',
    'minmax': 'min & max',
}

Extract and calculate quantities of interest¶

################# All together
######### Assemble plot data
mergecols = ['ISOwecc','ISO:Node','yearlmp']
###### Fixed DA: optrev_rev vs optcf_rev; orientations
program = 'PVvalueOptV4'
systemtype = 'fixed'
yearsun = 'tmy'

###### Must-run
pricecutoff = 'no'
dfplot = dfin.loc[
    (dfin.market == 'da')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Rev/OptCF_Rev','OptRev_Azimuth','OptRev_Tilt',
             'OptRev_CF/OptCF_CF', 'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfrt = dfin.loc[
    (dfin.market == 'rt')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Rev/OptCF_Rev','OptRev_Azimuth','OptRev_Tilt',
             'OptRev_CF/OptCF_CF', 'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfplot = dfplot.merge(dfrt, on=mergecols, suffixes=('(da)','(rt)'), how='outer')

###### Curtailable
pricecutoff = '0'
dfcurtail = dfin.loc[
    (dfin.market == 'da')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Azimuth','OptRev_Tilt',
             'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfrt = dfin.loc[
    (dfin.market == 'rt')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Azimuth','OptRev_Tilt',
             'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfcurtail = dfcurtail.merge(dfrt, on=mergecols, suffixes=('(da)','(rt)'))

dfplot = dfplot.merge(dfcurtail, on=mergecols, suffixes=('(mustrun)','(curtail)'))

###### Track, DA and RT: Rev_curtail/Rev_mustrun

program = 'PVvalueV8'
pricecutoff = '0'
systemtype = 'track'

##########
### Ratios
datum = 'Rev_curtail/Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] / dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail/CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] / dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

###############
### Differences
datum = 'Rev_curtail-Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] - dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail-CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] - dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')
    
# dfplot = dfplot.dropna(thresh=10).copy()

###############################
######### Historical irradiance
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor',
        'Revenue_dispatched','CapacityFactor_dispatched',
        'ValueAverage_dispatched','ValueFactor_dispatched',]
dicthist = {}
for market in ['da','rt']:
    ############ Revenue and CF ratios, fixed-tilt 
    ###### Must-run
    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue','CapacityFactor','ValueAverage','ValueFactor']:
        dicthist['{}_hist_optcf_f({})(mustrun)'.format(datum,market)] = df[datum]

    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == '0'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue','CapacityFactor','ValueAverage','ValueFactor']:
        dicthist['{}_hist_optrev_f({})(mustrun)'.format(datum,market)] = df[datum]
        
    ###### Curtailable
    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == 'opt0cutoff'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue_dispatched','CapacityFactor_dispatched',
                  'ValueAverage_dispatched','ValueFactor_dispatched']:
        dicthist['{}_hist_optcf_f({})(curtail)'.format(datum,market)] = df[datum]

    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == 'opt0cutoff'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue_dispatched','CapacityFactor_dispatched',
                  'ValueAverage_dispatched','ValueFactor_dispatched']:
        dicthist['{}_hist_optrev_f({})(curtail)'.format(datum,market)] = df[datum]

############ Tracking vs fixed CFopt, DA, must-run
######### must-run
for market in ['da', 'rt']:
    dftop = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == 'PVvalueV8')
        & (dfin.systemtype == 'track')
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dftop.index = dftop[['ISO:Node','yearlmp']]

    dfbot = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')#,
        & (dfin_optorient_histsun.mod1 == '0')
    #     ['ISO:Node','yearlmp','Revenue','CapacityFactor']
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dfbot.index = dfbot[['ISO:Node','yearlmp']]

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')
    dicthist['Revenue_hist_default_t({})(mustrun)'.format(market)] = dftop['Revenue']
    dicthist['Revenue_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['Revenue'] / dfbot['Revenue'])
    dicthist['CapacityFactor_hist_default_t({})(mustrun)'.format(market)] = dftop['CapacityFactor']
    dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['CapacityFactor'] / dfbot['CapacityFactor'])
    dicthist['Value_hist_default_t({})(mustrun)'.format(market)] = dftop['ValueAverage']
    dicthist['Value_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['ValueAverage'] / dfbot['ValueAverage'])
    dicthist['ValueFactor_hist_default_t({})(mustrun)'.format(market)] = dftop['ValueFactor']
    ###############
    ### Differences
    dicthist['Revenue_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['Revenue'] - dfbot['Revenue'])
    dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['CapacityFactor'] - dfbot['CapacityFactor'])
    dicthist['Value_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['ValueAverage'] - dfbot['ValueAverage'])
    

######### curtailable
for market in ['da', 'rt']:
    dftop = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == 'PVvalueV8')
        & (dfin.systemtype == 'track')
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dftop.index = dftop[['ISO:Node','yearlmp']]

    dfbot = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')#,
        & (dfin_optorient_histsun.mod1 == 'opt0cutoff')
    #     ['ISO:Node','yearlmp','Revenue','CapacityFactor']
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dfbot.index = dfbot[['ISO:Node','yearlmp']]

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')
    ###### Baseline = curtailable
    dicthist['Revenue_hist_default_t({})(curtail)'.format(market)] = dftop['Revenue_dispatched']
    dicthist['CapacityFactor_hist_default_t({})(curtail)'.format(market)] = dftop['CapacityFactor_dispatched']
    dicthist['Revenue_track(def)/fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['Revenue_dispatched'] / dfbot['Revenue_dispatched'])
    dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['CapacityFactor_dispatched'] / dfbot['CapacityFactor_dispatched'])
    dicthist['Value_hist_default_t({})(curtail)'.format(market)] = dftop['ValueAverage_dispatched']
    dicthist['ValueFactor_hist_default_t({})(curtail)'.format(market)] = dftop['ValueFactor_dispatched']
    ###### Baseline = mustrun
    dicthist['Revenue_track(def)/fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['Revenue_dispatched'] / dfbot['Revenue']) ### CHANGED dfbot from _Dispatched
    dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['CapacityFactor_dispatched'] / dfbot['CapacityFactor']) ### CHANGED dfbot from _Dispatched
    ###############
    ### Differences
    dicthist['Revenue_track(def)-fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['Revenue_dispatched'] - dfbot['Revenue_dispatched'])
    dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['CapacityFactor_dispatched'] - dfbot['CapacityFactor_dispatched'])
    dicthist['Revenue_track(def)-fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['Revenue_dispatched'] - dfbot['Revenue']) ### CHANGED dfbot from _Dispatched
    dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['CapacityFactor_dispatched'] - dfbot['CapacityFactor']) ### CHANGED dfbot from _Dispatched

### Merge them
dfmerge = pd.concat(dicthist,axis=1)

for market in ['da','rt']:
    ###### Take ratios
    ##### Must-run
    dfmerge['Rev_OptRev/OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['Revenue_hist_optrev_f({})(mustrun)'.format(market)] 
        / dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)])
    dfmerge['CF_OptRev/OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['CapacityFactor_hist_optrev_f({})(mustrun)'.format(market)] 
        / dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
    ##### Curtailable
    ### Baseline = curtailable
    dfmerge['Rev_OptRev/OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['Revenue_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    dfmerge['CF_OptRev/OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['CapacityFactor_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    ### Baseline = mustrun
    dfmerge['Rev_OptRev/OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)]) ### CHANGED from _Dispatched, curtail
    dfmerge['CF_OptRev/OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)]) ### CHANGED from _Dispatched,curtail
    ###############
    ### Differences
    ##### Must-run
    dfmerge['Rev_OptRev-OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['Revenue_hist_optrev_f({})(mustrun)'.format(market)] 
        - dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)])
    dfmerge['CF_OptRev-OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['CapacityFactor_hist_optrev_f({})(mustrun)'.format(market)] 
        - dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
    ##### Curtailable
    ### Baseline = curtailable
    dfmerge['Rev_OptRev-OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['Revenue_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    dfmerge['CF_OptRev-OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['CapacityFactor_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    ### Baseline = mustrun
    dfmerge['Rev_OptRev-OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)]) 
    dfmerge['CF_OptRev-OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
    

dfmerge.index = pd.MultiIndex.from_tuples(dfmerge.index, names=('ISO:Node', 'yearlmp'))
dfmerge.reset_index(inplace=True)

dfplot = dfplot.merge(dfmerge, on=['ISO:Node','yearlmp'])

/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:156: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

###### Fixed, DA and RT: Rev_curtail/Rev_mustrun

pricecutoff = '0'
systemtype = 'fixed'

##########
### Ratios
datum = 'Rev_curtail/Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] / dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail/CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] / dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')
    
###############
### Differences
datum = 'Rev_curtail-Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] - dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail-CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] - dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

############ Azimuth orientation for 1-axis-tracking sytems, vs CF-optimized fixed-tilt must-run
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor']
###### Must-run
for market in ['da','rt']:
    dftop = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == 'track')
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == 'azopt'),
        mergecols+data
    ].copy()


    dfbot = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0'),
        mergecols+data
    ].copy()

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')

    dfmerge = dftop.merge(
        dfbot, on=['ISOwecc','ISO:Node','yearlmp'], 
        suffixes=('_track,optrev,{},mustrun'.format(market), '_fixed,optcf,{},mustrun'.format(market)),
    )

    ### Calculate ratios
    dfmerge['CF_track,optrev,{},mustrun/CF_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        / dfmerge['CapacityFactor_fixed,optcf,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun/Rev_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        / dfmerge['Revenue_fixed,optcf,{},mustrun'.format(market)]
    )
    ###############
    ### Differences
    dfmerge['CF_track,optrev,{},mustrun-CF_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        - dfmerge['CapacityFactor_fixed,optcf,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun-Rev_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        - dfmerge['Revenue_fixed,optcf,{},mustrun'.format(market)]
    )
    
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[
            'CF_track,optrev,{},mustrun/CF_fixed,optcf,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun/Rev_fixed,optcf,{},mustrun'.format(market,market),
            'CF_track,optrev,{},mustrun-CF_fixed,optcf,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun-Rev_fixed,optcf,{},mustrun'.format(market,market),
        ]], 
        on=mergecols, how='outer',
    )

############ Azimuth orientation for 1-axis-tracking sytems, vs default track must-run
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor']
###### Must-run
for market in ['da','rt']:
    dftop = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == 'track')
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == 'azopt'),
        mergecols+data
    ].copy()

    dfbot = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == 'PVvalueV8')
        & (dfin.systemtype == 'track')
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp'),
        mergecols+data
    ].copy()

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')

    dfmerge = dftop.merge(
        dfbot, on=['ISOwecc','ISO:Node','yearlmp'], 
        suffixes=('_track,optrev,{},mustrun'.format(market), '_track,default,{},mustrun'.format(market)),
    )

    ### Calculate ratios
    dfmerge['CF_track,optrev,{},mustrun/CF_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        / dfmerge['CapacityFactor_track,default,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun/Rev_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        / dfmerge['Revenue_track,default,{},mustrun'.format(market)]
    )
    ###############
    ### Differences
    dfmerge['CF_track,optrev,{},mustrun-CF_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        - dfmerge['CapacityFactor_track,default,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun-Rev_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        - dfmerge['Revenue_track,default,{},mustrun'.format(market)]
    )

    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[
            'CF_track,optrev,{},mustrun/CF_track,default,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun/Rev_track,default,{},mustrun'.format(market,market),
            'CF_track,optrev,{},mustrun-CF_track,default,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun-Rev_track,default,{},mustrun'.format(market,market),
        ]], 
        on=mergecols, how='outer',
    )

####### Azimuth optimization for tracking systems
systemtype = 'track'
pricecutoff = 'no'
yearsun = 'tmy'
program = 'PVvalueOptV4'

dfmerge = dfin.loc[
    (dfin.program==program)
    &(dfin.systemtype==systemtype)
    &(dfin.pricecutoff==pricecutoff)
    &(dfin.yearsun==yearsun)
    &(dfin.market=='da'),
    mergecols+['OptCF_Azimuth']
].rename(columns={'OptCF_Azimuth':'OptCF_Azimuth(track)'})

for market in ['da','rt']:
    dfmerge = dfmerge.merge(
        dfin.loc[
            (dfin.program==program)
            &(dfin.systemtype==systemtype)
            &(dfin.pricecutoff==pricecutoff)
            &(dfin.yearsun==yearsun)
            &(dfin.market==market),
            mergecols+['OptRev_Azimuth']
        ].rename(columns={'OptRev_Azimuth':'OptRev_Azimuth(track)({})(mustrun)'.format(market)}),
        on=mergecols, how='outer')

for datum in ['OptCF_Azimuth(track)', 'OptRev_Azimuth(track)(da)(mustrun)', 
              'OptRev_Azimuth(track)(rt)(mustrun)', ]:
    dfmerge[datum] = dfmerge[datum].map(lambda x: x + 180 if x < 90 else x)
    
dfplot = dfplot.merge(dfmerge, on=mergecols, how='outer').copy()

Figures¶

Baseline results¶

########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CapacityFactor_hist_optcf_f(da)(mustrun)',
    'Revenue_hist_optcf_f(da)(mustrun)',
    'Revenue_hist_optcf_f(rt)(mustrun)',
    'ValueAverage_hist_optcf_f(da)(mustrun)',
    'ValueAverage_hist_optcf_f(rt)(mustrun)',
    'ValueFactor_hist_optcf_f(da)(mustrun)',
    'ValueFactor_hist_optcf_f(rt)(mustrun)',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))


### Column-indexed parameters
ncols = 4
# cols = [0,1,2,3]
ylim = [
    [0,0.3],
    [0,180], #[0,210], 
    [0,90], #[0,110],
    [0.5,1.7], #[0.6,1.8],
]

ylabel = [
    'CF',
    'Revenue',
    'Value',
    'VF',
]

note = [
    '[fraction]',
    '[$/kWac-yr]',
    '[$/MWh]',
    '[fraction]',
]

gridspec_kw = {'width_ratios': [1, 2, 2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            # medianmarker='_', mediansize=10, medianfacecolor='k'
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=1.2, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ### Add 1-line for VF
        ax[(row,-1)].axhline(1, lw=0.25, c='0.5')
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(0,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Quantities, CF-optimized fixed-tilt, must-run', weight='bold', y=1.07, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)

CAISO 2017

median

max

CapacityFactor_hist_optcf_f(da)(mustrun)

Revenue_hist_optcf_f(da)(mustrun)

Revenue_hist_optcf_f(rt)(mustrun)

ValueAverage_hist_optcf_f(da)(mustrun)

ValueAverage_hist_optcf_f(rt)(mustrun)

ValueFactor_hist_optcf_f(da)(mustrun)

ValueFactor_hist_optcf_f(rt)(mustrun)

Curtailment (fixed-tilt)¶

########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CF_curtail/CF_mustrun(fixed)(da)',
    'CF_curtail/CF_mustrun(fixed)(rt)',
    'Rev_curtail/Rev_mustrun(fixed)(da)',
    'Rev_curtail/Rev_mustrun(fixed)(rt)',
]
colindex = [0, 0, 1, 1]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 2
ylim = [
    [0.68, 1.02],
    [0.98, 1.32],
]

ylabel = [
    'Capacity Factor',
    'Revenue',
]

note = [
    '',
    '',
]
y1 = 1     # 1.2 if using note
y2 = 1.04  # 1.07 if using note

gridspec_kw = {'width_ratios': [2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
#             format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.1))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Curtailable vs. Must-run, fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)

CAISO 2017

median

max

CF_curtail/CF_mustrun(fixed)(da)

CF_curtail/CF_mustrun(fixed)(rt)

Rev_curtail/Rev_mustrun(fixed)(da)

Rev_curtail/Rev_mustrun(fixed)(rt)

Tracking¶

########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun',
    'CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun',
    'CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
    'Revenue_track(def)/fixed(optcf)_hist,da,mustrun',
    'Revenue_track(def)/fixed(optcf)_hist,rt,mustrun',
    'Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun',
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 4
ylim = [
    [0.92, 1.7],
    [0.92, 1.7],
    [0.92, 1.7],
    [0.92, 1.7],
]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue',
    'Revenue',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2   # 1.2 if using note, 1 if not
y2 = 1.07  # 1.07 if using note, 1.05 if not

gridspec_kw = {'width_ratios': [1, 2, 2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            # medianmarker='_', mediansize=10, medianfacecolor='k'
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.2))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ax[(row,col)].axhline(1, lw=0.25, c='0.5')
        
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, 1-ax track vs. fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)

CAISO 2017

median

max

CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun

CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun

CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun

Revenue_track(def)/fixed(optcf)_hist,da,mustrun

Revenue_track(def)/fixed(optcf)_hist,rt,mustrun

Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun

Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun

Orientation optimization, fixed-tilt¶

Orientation¶

########## Starting absolute values for track CF-opt
### Data-indexed parameters
data = [
    'OptCF_Azimuth(da)(mustrun)',
    'OptRev_Azimuth(da)(mustrun)',
    'OptRev_Azimuth(rt)(mustrun)',
    'OptRev_Azimuth(da)(curtail)',
    'OptRev_Azimuth(rt)(curtail)',
    'OptCF_Tilt(da)(mustrun)',
    'OptRev_Tilt(da)(mustrun)',
    'OptRev_Tilt(rt)(mustrun)',
    'OptRev_Tilt(da)(curtail)',
    'OptRev_Tilt(rt)(curtail)',
]
colindex = [0, 1, 1, 2, 2, 3, 4, 4, 5, 5,]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right',
             'right','left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],
         mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 
           0.7, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [[2011,2017],slice(None),slice(None),slice(None),slice(None),
            [2011,2017],slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [166, 255],
    [166, 255],
    [166, 255],
    [15, 57],
    [15, 57],
    [15, 57],
]
xlim = [
    [2017, 2018.9],
    [2009.4, 2018],
    [2009.4, 2018],
    [2017, 2018.9],
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [30, 30, 30, 15, 15, 15, ]
minlocs = [2, 2, 2, 3, 3, 3, ]

ylabel = [
    'Azimuth',
    'Azimuth',
    'Azimuth',
    'Tilt',
    'Tilt',
    'Tilt',
]

note = [
    'CF Opt.',
    'Revenue Opt. (must-run)',
    'Revenue Opt. (curtailable)',
    'CF Opt.',
    'Revenue Opt. (must-run)',
    'Revenue Opt. (curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [0.2, 2, 2, 0.2, 2, 2,], 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,1)].legend(
    handles=patches, loc='upper left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Optimized orientation, fixed-tilt', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)

CAISO 2017

median

max

OptCF_Azimuth(da)(mustrun)

OptRev_Azimuth(da)(mustrun)

OptRev_Azimuth(rt)(mustrun)

OptRev_Azimuth(da)(curtail)

OptRev_Azimuth(rt)(curtail)

OptCF_Tilt(da)(mustrun)

OptRev_Tilt(da)(mustrun)

CF and revenue impacts¶

########## Baseline = mustrun
### Data-indexed parameters
data = [
    'CF_OptRev/OptCF_hist,da,f,mustrun',
    'CF_OptRev/OptCF_hist,rt,f,mustrun',
    'CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun',
    'CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
    'Rev_OptRev/OptCF_hist,da,f,mustrun',
    'Rev_OptRev/OptCF_hist,rt,f,mustrun',
    'Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun',
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
]
colindex = [0, 0, 1, 1, 2, 2, 3, 3,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',
             'left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],
         mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),
            slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [0.63, 1.02],
    [0.63, 1.02],
    [0.98,1.37],
    [0.98,1.37],
]
xlim = [
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [0.1, 0.1, 0.1, 0.1]
minlocs = [2, 2, 2, 2,]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue',
    'Revenue',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [2, 2, 2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Revenue-opt. vs. CF-opt., must-run baseline', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)

CAISO 2017

median

max

CF_OptRev/OptCF_hist,da,f,mustrun

CF_OptRev/OptCF_hist,rt,f,mustrun

CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun

CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun

Rev_OptRev/OptCF_hist,da,f,mustrun

Rev_OptRev/OptCF_hist,rt,f,mustrun

Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun

Results synthesis for CAISO 2017¶

### Rename labels
def labelfy(datum):
    out = ''
    
    if '_f' in datum:
        out += 'Fixed\n'
    else:
        out += 'Track\n'
    
    if ('optrev' in datum) and ('_f' in datum):
        out += 'Rev. opt.\n'
    elif '_f' in datum:
        out += 'CF opt.\n'
    else:
        out += 'Default\n'
    
    if '(curtail)' in datum:
        out += 'Curtail'
    else:
        out += 'Must-run'
    
    return out

columnorder = [
    'Fixed\nCF opt.\nCurtail', 
    'Fixed\nRev. opt.\nMust-run',
    'Fixed\nRev. opt.\nCurtail', 
    'Track\nDefault\nMust-run',
    'Track\nDefault\nCurtail',
]

########## Absolute values
iso = 'CAISO'
year = 2017

squeeze = 0.3
ncols = 2
gridspec_kw = {'width_ratios': [2, 2], 'wspace': 0.1}
figsize = (12,4)
dpi = None

###### Column 0: Historical, baseline
datum = 'Revenue'
markets = ['da','rt']
data_baseline = [
    ### Column 0, vs years
    'Revenue_hist_optcf_f(da)(mustrun)',
    'Revenue_hist_optcf_f(rt)(mustrun)',
]

### Data-indexed parameters
ylim = [0,145]
ylabel = 'Revenue [$/kWac-yr]'

###### Column 1: single year, TOS
data_tos = [
    ### Column 1, single year, row 0
    'Revenue_dispatched_hist_optcf_f(da)(curtail)',
    'Revenue_dispatched_hist_optcf_f(rt)(curtail)',
    'Revenue_hist_optrev_f(da)(mustrun)',
    'Revenue_hist_optrev_f(rt)(mustrun)',
    'Revenue_dispatched_hist_optrev_f(da)(curtail)',
    'Revenue_dispatched_hist_optrev_f(rt)(curtail)',
    'Revenue_hist_default_t(da)(mustrun)',
    'Revenue_hist_default_t(rt)(mustrun)',
    'Revenue_hist_default_t(da)(curtail)',
    'Revenue_hist_default_t(rt)(curtail)',
]
dfright = pd.melt(
    dfplot.loc[(dfplot.ISOwecc==iso)&(dfplot.yearlmp==year),['ISO:Node']+data_tos],
    id_vars=['ISO:Node']
)
dfright['market'] = dfright.variable.map(lambda x: 'da' if '(da)' in x else 'rt')
dfright['label'] = dfright.variable.map(labelfy)
dfright['datum'] = dfright.variable.map(lambda x: x.split('_')[0])

### Plot it
plt.close()
f,ax = plt.subplots(1,ncols,sharex='col',sharey='row', gridspec_kw=gridspec_kw, 
                    figsize=figsize, dpi=dpi,
                   )
### Column 0: Baseline data vs years (fixed, CFopt, must-run)
medians = []
for column in data_baseline:
    dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
               .pivot(index='ISO:Node',columns='yearlmp',values=column))

    pvvm.plots.plotquarthist(
        ax=ax[0], dfplot=dfframe, bootstrap=bootstrap, density=True,
        hist_range=ylim,
        histcolor=(mc['da'] if '(da)' in column else mc['rt']), 
        direction=('left' if '(da)' in column else 'right'), 
        squeeze=squeeze,
        quartpad=(-0.1 if '(da)' in column else 0.1),
        histpad=(-0.15 if '(da)' in column else 0.15),
        # medianmarker='_', mediansize=10, medianfacecolor='k'
    )
### Column 1: Single-year data vs TOS strategy
for market in markets:
    dfframe = dfright.loc[
        (dfright.market==market)&(dfright.datum==datum)
    ].pivot(index='ISO:Node',columns='label',values='value')[columnorder]
    medians.append(dfframe[columnorder[-1]].median())

    pvvm.plots.plotquarthist(
        ax=ax[1], dfplot=dfframe, bootstrap=bootstrap, density=True,
        x_locations=range(dfframe.shape[1]),
        hist_range=ylim,
        histcolor=mc[market], 
        direction=('left' if market == 'da' else 'right'), 
        squeeze=squeeze,
        quartpad=(-0.06 if market == 'da' else 0.06),
        histpad=(-0.11 if market == 'da' else 0.11),
        # medianmarker='_', mediansize=10, medianfacecolor='k'
    )

### Format axis
### Left
ax[0].set_ylabel(ylabel, weight='bold')
ax[0].set_xlim(2009.4,2018)
ax[0].set_xticks([2010,2012,2014,2016])
ax[0].set_xticklabels(['2010','2012','2014','2016'], rotation=0, ha='center')
ax[0].xaxis.set_minor_locator(AutoMinorLocator(2))
ax[0].set_ylim(0)
### Right
ax[1].set_xlim(-0.6,4.6)

### Grid lines
for i in range(2):
#     ax[i].grid(which='major', axis='y', zorder=-1, lw=0.5, ls=(0,(3,6)))
    ax[i].grid(which='major', axis='y', zorder=-1, lw=0.25)
    
### Add title
ax[0].set_title('Baseline: Fixed, CF opt., must-run', weight='bold', size='x-large')
ax[1].set_title('2017', weight='bold', size='x-large')

pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[0].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7)

plt.show()

for column in data_tos:
    print(column)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[column].median().unstack('ISOwecc'))

Revenue_dispatched_hist_optcf_f(da)(curtail)

Revenue_dispatched_hist_optcf_f(rt)(curtail)

Revenue_hist_optrev_f(da)(mustrun)

Revenue_hist_optrev_f(rt)(mustrun)

Revenue_dispatched_hist_optrev_f(da)(curtail)

Revenue_dispatched_hist_optrev_f(rt)(curtail)

Revenue_hist_default_t(da)(mustrun)

Revenue_hist_default_t(rt)(mustrun)

Revenue_hist_default_t(da)(curtail)

Revenue_hist_default_t(rt)(curtail)

Orientation optimization, 1-axis tracking¶

Orientation¶

########## Starting absolute values for track CF-opt
systemtype = 'track'
pricecutoff = 'no'
yearsun = 'tmy'
program = 'PVvalueOptV4'

### Data-indexed parameters
data = [
    'OptCF_Azimuth(track)',
    'OptRev_Azimuth(track)(da)(mustrun)',
    'OptRev_Azimuth(track)(rt)(mustrun)',
]
colindex = [0, 1, 1,]
colindex = dict(zip(data, colindex))
direction = ['right','left','right',]
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [[2011,2017],slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [90, 270],
    [90, 270],
]
xlim = [
    [2017, 2018.9],
    [2009.4, 2018],
]

majlocs = [45, 45,]
minlocs = [1, 1,]

ylabel = [
    'Azimuth',
    'Azimuth',
]

note = [
    'CF Opt.',
    'Revenue Opt. (must-run)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [0.2, 2,], 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=True, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=5, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Optimized orientation, 1-axis track', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)

CAISO 2017

median

max

OptCF_Azimuth(track)

OptRev_Azimuth(track)(da)(mustrun)

OptRev_Azimuth(track)(rt)(mustrun)

CF and revenue impacts¶

########## Starting absolute values for track CF-opt
### Data-indexed parameters
data = [
    'CF_track,optrev,da,mustrun/CF_track,default,da,mustrun',
    'CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun',
    'Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun',
    'Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun',
]
colindex = [0, 0, 1, 1,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [0.90, 1.04],
    [0.96,1.10],
]
xlim = [
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [0.05,0.05,0.05,0.05,]
minlocs = [2,2,2,2,]

ylabel = [
    'Capacity Factor',
    'Revenue',
]

note = [
    '(must-run)',
    '(must-run)',
#     '(curtailable)',
#     '(curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(0,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Revenue-opt. vs. default, 1-axis track, must-run', 
          weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

print('ISONE 2014')
display(dfplot.loc[(dfplot.ISOwecc=='ISONE')&(dfplot.yearlmp==2014),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)

ISONE 2014

median

max

CF_track,optrev,da,mustrun/CF_track,default,da,mustrun

CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun

Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun

Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun

Supplementary information¶

Geographic data¶

dfmap = dfplot.merge(
    dfin[['ISO:Node','yearlmp','Latitude','Longitude']].drop_duplicates(['ISO:Node','yearlmp']),
    on=['ISO:Node','yearlmp'], how='left'
).copy()

maplabel = {
    'OptRev_Azimuth(rt)(mustrun)': 'Azimuth [°]',
    'OptRev_Azimuth(rt)(curtail)': 'Azimuth [°]',
    'OptRev_Tilt(rt)(mustrun)': 'Tilt [°]',
    'OptRev_Tilt(rt)(curtail)': 'Tilt [°]',
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': 'Revenue ratio\ntrack/fixed',
    'Rev_curtail/Rev_mustrun(fixed)(rt)': 'Revenue ratio\ncurtail/must-run',
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': 'Revenue ratio\nRevenue-opt/CF-opt'
}

zrange = {
    'OptRev_Azimuth(rt)(mustrun)': [90,270],
    'OptRev_Tilt(rt)(mustrun)': [15,57],
    'OptRev_Azimuth(rt)(curtail)': [120,240],
    'OptRev_Tilt(rt)(curtail)': [15,47],
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': [1., 1.7],
    'Rev_curtail/Rev_mustrun(fixed)(rt)': [0.99,1.32],
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': [0.99,1.37],
}
zticks = {
    'OptRev_Azimuth(rt)(mustrun)': [90,135,180,225,270],
    'OptRev_Tilt(rt)(mustrun)': [20,30,40,50,],
    'OptRev_Azimuth(rt)(curtail)': [120,150,180,210,240],
    'OptRev_Tilt(rt)(curtail)': [15,25,35,45],
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': list(np.arange(1,1.71,0.1)),
    'Rev_curtail/Rev_mustrun(fixed)(rt)': [1.,1.1,1.2,1.3],
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': [1.,1.1,1.2,1.3]
}
cmap = plt.cm.coolwarm#{
#     'OptRev_Azimuth(rt)(mustrun)': plt.cm.coolwarm,
#     'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': plt.cm.coolwarm,
#     'Rev_curtail/Rev_mustrun(fixed)(rt)': plt.cm.coolwarm,
# }

data = [
#     'OptRev_Azimuth(rt)(mustrun)',
#     'OptRev_Tilt(rt)(mustrun)',
    'OptRev_Azimuth(rt)(curtail)',
    'OptRev_Tilt(rt)(curtail)',
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
    'Rev_curtail/Rev_mustrun(fixed)(rt)',
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
]
for datum in data:
    print('{}\n{}\n{}'.format('#'*30,datum,'#'*30))
    for year in range(2010,2018):
        plt.close()
        f,ax = pvvm.plots.plotusascattermap(
            dfmap.loc[dfmap.yearlmp==year], datum,
            zrange=zrange[datum],
            # colorbartitle=maplabel[datum],
            colorbarhist=False,
            cmap=cmap,
        )
        cbar, chist = pvvm.plots.addcolorbarhist(
            f=f, ax0=ax, data=dfmap.loc[dfmap.yearlmp==year,datum],
            title=maplabel[datum], cmap=cmap,
            vmin=zrange[datum][0], vmax=zrange[datum][1]
        )
        cbar.yaxis.set_ticks(zticks[datum])

        ax.set_title(year, y=0.93, fontsize='x-large', weight='bold')
        ax.annotate(
            datum.replace('/','\n/ '), 
            xy=(0.03,0.05), xycoords='axes fraction',
            ha='left', va='bottom', fontsize='medium')

        plt.show()

##############################
OptRev_Azimuth(rt)(curtail)
##############################

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

##############################
OptRev_Tilt(rt)(curtail)
##############################

Impact of temporal output shaping as differences instead of ratios¶

Curtailment¶

########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CF_curtail-CF_mustrun(fixed)(da)',
    'CF_curtail-CF_mustrun(fixed)(rt)',
    'Rev_curtail-Rev_mustrun(fixed)(da)',
    'Rev_curtail-Rev_mustrun(fixed)(rt)',
]
colindex = [0, 0, 1, 1]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 2
ylim = [
    [-0.08, 0.01],
    [-1, 15],
]

ylabel = [
    'Capacity Factor',
    'Revenue [$/kWac-yr]',
]

note = [
    '',
    '',
]
y1 = 1     # 1.2 if using note
y2 = 1.04  # 1.07 if using note

gridspec_kw = {'width_ratios': [2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
#             format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
#         ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.1))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ax[(row,col)].axhline(0, lw=0.25, c='0.5')

ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, curtailable – must-run, fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

Tracking¶

########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CapacityFactor_track(def)-fixed(optcf)_hist,da,mustrun',
    'CapacityFactor_track(def)-fixed(optcf)_hist,da,curtail,baselinemustrun',
    'CapacityFactor_track(def)-fixed(optcf)_hist,rt,curtail,baselinemustrun',
    'Revenue_track(def)-fixed(optcf)_hist,da,mustrun',
    'Revenue_track(def)-fixed(optcf)_hist,rt,mustrun',
    'Revenue_track(def)-fixed(optcf)_hist,da,curtail,baselinemustrun',
    'Revenue_track(def)-fixed(optcf)_hist,rt,curtail,baselinemustrun',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 4
ylim = [
    [-0.03,0.08],
    [-0.03,0.08],
    [-3,40],
    [-3,40],
]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue [$/kWac-yr]',
    'Revenue [$/kWac-yr]',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2   # 1.2 if using note, 1 if not
y2 = 1.07  # 1.07 if using note, 1.05 if not

gridspec_kw = {'width_ratios': [1, 2, 2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            # medianmarker='_', mediansize=10, medianfacecolor='k'
        )
#         ### NEW: Add revenue cutoff line
#         if datum.startswith('Revenue') and (',da,' in datum):
#             ax[(row,colindex[datum])].axhline(1.066, lw=0.25, ls=(0,(6,6)), c='0.5')

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
#         ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.2))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ax[(row,col)].axhline(0, lw=0.25, c='0.5')

ax[(0,0)].yaxis.set_major_locator(MultipleLocator(0.05))
ax[(0,1)].yaxis.set_major_locator(MultipleLocator(0.05))
ax[(0,2)].yaxis.set_major_locator(MultipleLocator(10))
ax[(0,3)].yaxis.set_major_locator(MultipleLocator(10))
ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0,))
ax[(0,1)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0,))

pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, 1-ax track – fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

Orientation optimization, fixed-tilt¶

########## Baseline = mustrun
### Data-indexed parameters
data = [
    'CF_OptRev-OptCF_hist,da,f,mustrun',
    'CF_OptRev-OptCF_hist,rt,f,mustrun',
    'CF_OptRev-OptCF_hist,da,f,curtail,baselinemustrun',
    'CF_OptRev-OptCF_hist,rt,f,curtail,baselinemustrun',
    'Rev_OptRev-OptCF_hist,da,f,mustrun',
    'Rev_OptRev-OptCF_hist,rt,f,mustrun',
    'Rev_OptRev-OptCF_hist,da,f,curtail,baselinemustrun',
    'Rev_OptRev-OptCF_hist,rt,f,curtail,baselinemustrun',
]
colindex = [0, 0, 1, 1, 2, 2, 3, 3,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',
             'left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],
         mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),
            slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [-0.09, 0.01],
    [-0.09, 0.01],
    [-1,20],
    [-1,20],
]
xlim = [
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [0.05, 0.05, 10, 10]
minlocs = [2, 2, 2, 2,]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue [$/kWac-yr]',
    'Revenue [$/kWac-yr]',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [2, 2, 2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
#         ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
        ax[(row,col)].axhline(0, lw=0.25, c='0.5')

ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
ax[(0,1)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
    
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, revenue-optimized – CF-optimized', weight='bold', y=y2, fontsize='xx-large')

plt.show()

/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

	CapacityFactor_hist_optcf_f(da)(mustrun)	Revenue_hist_optcf_f(da)(mustrun)	Revenue_hist_optcf_f(rt)(mustrun)	ValueAverage_hist_optcf_f(da)(mustrun)	ValueAverage_hist_optcf_f(rt)(mustrun)	ValueFactor_hist_optcf_f(da)(mustrun)	ValueFactor_hist_optcf_f(rt)(mustrun)
count	2209.000000	2209.000000	2209.000000	2209.000000	2209.000000	2209.000000	2209.000000
mean	0.246188	59.642007	53.883991	27.708089	25.040576	0.801583	0.788523
std	0.011704	6.841004	7.212112	3.342419	3.514949	0.062013	0.079220
min	0.187778	28.469182	27.826508	12.993617	13.054663	0.451965	0.469798
2.5%	0.225073	45.610871	40.140121	20.772181	18.265009	0.671348	0.639226
25%	0.240347	56.143990	49.279463	25.543496	22.333303	0.761790	0.728410
50%	0.245543	59.215096	52.814589	27.703784	25.011672	0.816433	0.811701
75%	0.252588	63.347934	58.849791	29.722195	27.819860	0.843112	0.851000
97.5%	0.274404	75.239969	69.971012	34.382648	31.960051	0.915864	0.905468
max	0.282761	82.950998	74.620503	40.043755	35.075066	0.976529	0.937086

	CapacityFactor_hist_optcf_f(da)(mustrun)							Revenue_hist_optcf_f(da)(mustrun)							Revenue_hist_optcf_f(rt)(mustrun)							ValueAverage_hist_optcf_f(da)(mustrun)							ValueAverage_hist_optcf_f(rt)(mustrun)							ValueFactor_hist_optcf_f(da)(mustrun)							ValueFactor_hist_optcf_f(rt)(mustrun)
ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	0.230972	NaN	0.193482	0.198847	0.194502	0.198551	NaN	91.630257	NaN	105.497863	77.500738	112.758798	91.734709	NaN	94.208201	NaN	NaN	74.620116	117.453727	90.926518	NaN	45.325221	NaN	62.599238	43.293228	66.853404	54.546457	NaN	46.945908	NaN	NaN	41.735509	67.335810	53.880279	NaN	1.109605	NaN	1.138015	1.219608	1.152851	1.165117	NaN	1.062646	NaN	NaN	1.222089	1.144497	1.177101	NaN
2011	0.243936	0.234866	0.190098	0.189981	0.189608	0.188582	NaN	86.103130	163.255041	94.790453	69.637654	102.834552	89.623317	NaN	80.671016	147.202962	95.315083	67.368666	102.822357	90.472253	NaN	40.417082	79.033268	56.953720	41.132766	61.773388	54.993100	NaN	37.881238	71.076056	57.200568	39.981424	62.023122	56.037231	NaN	1.193562	1.591313	1.128503	1.214730	1.150326	1.207836	NaN	1.120612	1.533552	1.126617	1.212205	1.177433	1.224964	NaN
2012	0.244936	0.232577	0.199708	0.210066	0.196966	0.203782	NaN	76.214457	82.804797	75.495416	60.433441	79.442607	70.136512	NaN	76.621147	70.834097	75.994444	59.939321	81.261827	71.671996	NaN	35.591168	40.427457	43.116905	33.426375	46.395278	40.112320	NaN	35.546166	34.519891	43.572244	33.230227	47.427459	40.858529	NaN	1.139462	1.376841	1.115548	1.175683	1.150527	1.165354	NaN	1.145248	1.290854	1.128399	1.190303	1.184282	1.177129	NaN
2013	0.258688	0.226295	0.198371	0.195050	0.195548	0.194807	NaN	107.022227	79.332946	109.051458	61.371157	102.792563	72.538165	NaN	98.699543	74.592091	108.962743	60.310346	103.703793	73.717128	NaN	47.221461	39.773405	63.002813	36.261442	60.355995	44.514600	NaN	43.754006	37.518670	62.770983	35.717012	61.095318	44.325946	NaN	1.059504	1.182064	1.077531	1.137146	1.133439	1.153043	NaN	1.041000	1.160513	1.077664	1.135742	1.166363	1.164494	NaN
2014	0.245462	0.222564	0.194251	0.204298	0.189929	0.193770	NaN	108.886657	88.954743	119.572237	76.334670	113.466331	87.085898	NaN	99.847372	81.070067	111.806277	72.871556	108.451109	87.549573	NaN	50.624526	45.230950	70.751555	43.106767	68.232393	52.933508	NaN	47.074163	41.357884	66.180022	41.062071	66.128568	53.510857	NaN	1.022801	1.137982	1.069726	1.101950	1.082699	1.086669	NaN	0.992912	1.087337	1.027523	1.069602	1.110393	1.110727	NaN
2015	0.248991	0.213987	0.201143	0.203341	0.199722	0.196122	0.226895	73.239214	67.027475	81.934695	55.204096	79.654977	67.236213	60.160760	70.537787	53.928449	77.476387	54.091967	76.588783	64.386584	45.203972	33.848354	35.470877	46.708785	31.837814	45.406957	39.430827	30.262247	33.193210	28.553451	44.117595	30.718069	43.297521	37.790873	23.187285	0.993296	1.343341	1.087579	1.146485	1.152415	1.164359	0.955955	0.976788	1.196270	1.054393	1.156216	1.129825	1.144031	1.069051
2016	0.248407	0.227279	0.209589	0.212410	0.206703	0.201994	0.233462	59.331690	59.706667	61.372094	55.933880	61.697934	57.699422	50.692495	55.657016	55.293798	57.768955	54.724383	59.509091	57.117897	40.173297	27.562865	29.540111	33.328241	30.620093	33.805654	33.158103	24.467022	25.910069	27.542636	31.430099	29.891158	32.537043	32.603484	19.214541	0.919551	1.311012	1.099718	1.173928	1.157107	1.169964	0.872516	0.870209	1.276788	1.071098	1.177482	1.164329	1.165288	0.850056
2017	0.245543	0.226272	0.199129	0.208934	0.197039	0.199729	0.254154	59.215096	58.688937	60.681659	57.310864	60.449408	57.115360	50.727470	52.814589	55.402269	57.717520	57.560303	56.736561	57.307566	37.422478	27.703784	29.010892	34.789771	32.132992	35.720484	33.341268	24.026835	25.011672	27.621548	33.125203	32.137731	33.042092	33.385809	19.410569	0.816433	1.232272	1.048331	1.173494	1.118062	1.137926	0.754621	0.811701	1.184371	0.988141	1.191962	1.051551	1.144193	0.741065

	CapacityFactor_hist_optcf_f(da)(mustrun)							Revenue_hist_optcf_f(da)(mustrun)							Revenue_hist_optcf_f(rt)(mustrun)							ValueAverage_hist_optcf_f(da)(mustrun)							ValueAverage_hist_optcf_f(rt)(mustrun)							ValueFactor_hist_optcf_f(da)(mustrun)							ValueFactor_hist_optcf_f(rt)(mustrun)
ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	0.277866	NaN	0.207146	0.225963	0.206615	0.228378	NaN	116.851289	NaN	117.537375	113.244225	162.037417	174.184442	NaN	130.018708	NaN	NaN	114.945681	160.141739	164.998095	NaN	51.138572	NaN	68.874514	70.606498	91.530305	90.246885	NaN	56.869385	NaN	NaN	71.667337	89.056471	85.487338	NaN	1.139584	NaN	1.153784	1.304819	1.198685	1.357076	NaN	1.179287	NaN	NaN	1.370531	1.210511	1.333850	NaN
2011	0.285192	0.279049	0.205588	0.223398	0.206670	0.222986	NaN	106.351969	426.367353	105.779767	89.751867	145.127694	152.744892	NaN	99.718389	326.267108	108.772527	87.924704	153.641869	142.928318	NaN	46.800609	180.349037	61.840130	56.492077	83.694936	80.060863	NaN	44.866865	138.007656	61.772892	55.342015	87.563832	74.915530	NaN	1.253313	1.779045	1.147259	1.301418	1.263254	1.368157	NaN	1.237704	1.792053	1.155443	1.381830	1.289410	1.360069	NaN
2012	0.281405	0.273209	0.207713	0.231437	0.208879	0.218694	NaN	120.540637	778.858911	87.800296	74.783118	122.350396	137.166419	NaN	120.537862	694.822127	91.392987	82.178646	123.838466	138.588866	NaN	54.646675	336.886631	49.998567	47.209960	66.694580	71.803614	NaN	54.572420	300.006977	51.676310	51.878695	68.664584	72.548234	NaN	1.367523	1.940491	1.173878	1.326116	1.341058	1.272452	NaN	1.404304	2.121927	1.213145	1.361444	1.310590	1.285923	NaN
2013	0.283820	0.269719	0.206275	0.217156	0.210662	0.214248	NaN	126.315515	295.869659	124.496771	78.324788	156.028937	122.917740	NaN	115.165879	219.394761	121.951244	79.830470	204.811975	125.289742	NaN	54.949088	128.588504	69.726218	46.239649	86.942269	67.635080	NaN	51.113866	95.318513	68.044938	46.308729	114.125099	68.940266	NaN	1.131657	1.813841	1.130427	1.253033	1.191331	1.282994	NaN	1.122203	1.701891	1.096285	1.282900	1.274053	1.364994	NaN
2014	0.279916	0.265088	0.203394	0.223683	0.206444	0.215460	NaN	143.808370	292.514372	127.472113	114.593187	144.684377	193.165455	NaN	132.967096	197.115729	120.849700	114.276118	161.456743	187.543788	NaN	65.550925	137.372822	75.338511	64.177441	84.260185	104.543246	NaN	61.328208	89.325593	70.148506	63.999868	91.952168	101.926090	NaN	1.075579	1.664182	1.153818	1.249215	1.131118	1.167482	NaN	1.079433	1.532755	1.128290	1.252162	1.173316	1.314645	NaN
2015	0.280158	0.255952	0.209564	0.219845	0.211239	0.211704	0.260411	111.083014	104.810763	101.922449	71.453293	109.363575	132.634029	68.143543	110.014158	87.213937	83.699451	70.988370	105.433792	128.250100	52.701057	51.850500	49.667198	56.960907	39.863414	61.837104	72.657013	35.123601	50.599382	48.368216	48.159494	40.116250	63.448290	70.404004	25.880763	1.085500	1.534029	1.202821	1.244111	1.305214	1.302858	1.045935	1.114281	1.528232	1.109420	1.328237	1.516640	1.457411	1.142447
2016	0.281632	0.262511	0.219156	0.231624	0.222363	0.225568	0.276496	67.441523	128.198562	66.022374	73.002232	91.640258	120.496917	62.088274	64.641734	141.959905	62.485980	72.925825	98.416609	111.255032	58.409003	33.016643	65.213393	35.491711	39.262600	52.523487	62.346818	28.823508	33.178101	72.213658	33.983111	39.221507	56.407343	57.564935	26.436307	1.000511	1.727105	1.131670	1.266791	1.626424	1.300367	0.980711	0.971997	1.796781	1.214950	1.365266	1.703000	1.290563	1.045332
2017	0.282761	0.267850	0.207351	0.227381	0.209204	0.226219	0.276142	82.950998	104.163372	64.060605	82.061544	86.033215	87.835021	63.711292	74.620503	128.990140	62.265714	86.511683	77.479656	80.494622	60.736481	40.043755	52.523915	37.047420	43.651335	47.783136	47.560762	32.996176	35.075066	63.937091	35.346495	44.407004	43.032461	47.245435	28.116618	0.976529	1.507225	1.207329	1.310353	1.405340	1.286612	0.940674	0.937086	1.620960	1.482201	1.371905	1.447970	1.287909	1.073048

ISOwecc	CAISO								ERCOT							ISONE								MISO								NYISO								PJM								WECC -CAISO
yearlmp	2010	2011	2012	2013	2014	2015	2016	2017	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2015	2016	2017
count	2118.000000	2121.000000	2150.000000	2204.000000	2234.000000	2236.000000	2237.000000	2209.000000	1559.000000	1563.000000	1567.000000	1569.000000	1570.000000	1570.000000	1563.000000	409.000000	437.000000	499.000000	593.000000	612.000000	746.000000	829.000000	966.000000	179.000000	192.000000	197.000000	200.000000	368.000000	370.000000	386.000000	378.000000	402.000000	412.000000	424.000000	430.000000	434.000000	435.000000	436.000000	436.000000	4288.000000	4366.000000	4967.000000	5044.000000	4936.000000	4857.000000	4741.000000	4686.000000	1049.000000	1824.000000	3461.000000
mean	0.232652	0.245190	0.244435	0.258043	0.246584	0.248396	0.247330	0.246188	0.237353	0.233811	0.229137	0.224395	0.216660	0.229445	0.228360	0.190555	0.188857	0.197713	0.195224	0.191377	0.199974	0.207266	0.197753	0.197341	0.190831	0.207693	0.194743	0.202065	0.201792	0.209760	0.207154	0.189120	0.186542	0.193139	0.189843	0.187356	0.196382	0.203475	0.193788	0.198312	0.190271	0.202780	0.193415	0.193326	0.195856	0.203491	0.199148	0.221632	0.236713	0.238426
std	0.014412	0.012606	0.012300	0.008757	0.012197	0.009014	0.012372	0.011704	0.010747	0.010504	0.012283	0.011707	0.010886	0.009475	0.010145	0.009408	0.006664	0.005534	0.008408	0.007473	0.004207	0.006670	0.005155	0.009304	0.009098	0.010889	0.011114	0.012111	0.007098	0.010169	0.008433	0.014195	0.012401	0.008876	0.013602	0.010603	0.008383	0.009424	0.009766	0.010938	0.012499	0.007841	0.010048	0.009472	0.006770	0.008252	0.008816	0.015364	0.024022	0.030500
min	0.173918	0.193692	0.171110	0.203060	0.195900	0.199669	0.196407	0.187778	0.219438	0.212187	0.206964	0.197210	0.198289	0.210270	0.208513	0.161775	0.168907	0.181200	0.169681	0.169265	0.185596	0.183795	0.178488	0.174960	0.170079	0.176083	0.165189	0.154668	0.176217	0.183681	0.175909	0.164429	0.163120	0.174881	0.161536	0.166203	0.178952	0.185901	0.173762	0.169249	0.162624	0.177008	0.160435	0.169993	0.177850	0.183756	0.175545	0.175029	0.160074	0.165343
2.5%	0.208335	0.222666	0.223061	0.238273	0.225968	0.234869	0.219041	0.225073	0.224218	0.217323	0.210992	0.204041	0.202336	0.215856	0.214852	0.168085	0.174743	0.185035	0.175755	0.174051	0.188857	0.191418	0.184829	0.180039	0.176544	0.183455	0.171857	0.176615	0.183624	0.189082	0.188677	0.166983	0.165511	0.177199	0.165342	0.170124	0.179592	0.186832	0.177004	0.178770	0.170649	0.185958	0.172550	0.174672	0.182475	0.189725	0.182668	0.184294	0.177503	0.177094
25%	0.224480	0.238225	0.239986	0.255004	0.238837	0.242646	0.240335	0.240347	0.231548	0.228076	0.222471	0.217984	0.210657	0.223966	0.222721	0.188044	0.185747	0.194775	0.191531	0.188172	0.198717	0.204839	0.196382	0.188936	0.185344	0.198671	0.186321	0.193984	0.199122	0.203121	0.202481	0.174682	0.173456	0.184431	0.176722	0.176092	0.187735	0.194185	0.183894	0.190996	0.181061	0.197812	0.187423	0.187713	0.190953	0.197538	0.193094	0.214574	0.223098	0.221056
50%	0.230972	0.243936	0.244936	0.258688	0.245462	0.248991	0.248407	0.245543	0.234866	0.232577	0.226295	0.222564	0.213987	0.227279	0.226272	0.193482	0.190098	0.199708	0.198371	0.194251	0.201143	0.209589	0.199129	0.198847	0.189981	0.210066	0.195050	0.204298	0.203341	0.212410	0.208934	0.194502	0.189608	0.196966	0.195548	0.189929	0.199722	0.206703	0.197039	0.198551	0.188582	0.203782	0.194807	0.193770	0.196122	0.201994	0.199729	0.226895	0.233462	0.254154
75%	0.241740	0.252387	0.249604	0.262092	0.255667	0.252410	0.255109	0.252588	0.239234	0.236262	0.231392	0.228012	0.218764	0.232245	0.230555	0.197123	0.193722	0.201651	0.201684	0.196809	0.202754	0.211578	0.201086	0.203396	0.195544	0.216550	0.204496	0.211216	0.206093	0.216798	0.213269	0.202244	0.198042	0.199148	0.201630	0.195815	0.202614	0.212031	0.202601	0.204765	0.197942	0.208652	0.201026	0.199638	0.201261	0.210079	0.204569	0.231159	0.260125	0.262199
97.5%	0.269139	0.277368	0.271449	0.275072	0.271443	0.269770	0.272408	0.274404	0.271558	0.264260	0.263887	0.256828	0.247668	0.257023	0.260103	0.201700	0.198671	0.204188	0.205412	0.200667	0.205269	0.215520	0.204218	0.212018	0.214101	0.223600	0.210027	0.218603	0.212175	0.224108	0.219534	0.206602	0.203847	0.206583	0.207266	0.204395	0.209293	0.215591	0.205754	0.221948	0.217520	0.215017	0.209066	0.210115	0.206933	0.219446	0.216040	0.243591	0.267128	0.269153
max	0.277866	0.285192	0.281405	0.283820	0.279916	0.280158	0.281632	0.282761	0.279049	0.273209	0.269719	0.265088	0.255952	0.262511	0.267850	0.207146	0.205588	0.207713	0.206275	0.203394	0.209564	0.219156	0.207351	0.225963	0.223398	0.231437	0.217156	0.223683	0.219845	0.231624	0.227381	0.206615	0.206670	0.208879	0.210662	0.206444	0.211239	0.222363	0.209204	0.228378	0.222986	0.218694	0.214248	0.215460	0.211704	0.225568	0.226219	0.260411	0.276496	0.276142

ISOwecc	CAISO								ERCOT							ISONE								MISO								NYISO								PJM								WECC -CAISO
yearlmp	2010	2011	2012	2013	2014	2015	2016	2017	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2015	2016	2017
count	2118.000000	2121.000000	2150.000000	2204.000000	2234.000000	2236.000000	2237.000000	2209.000000	1559.000000	1563.000000	1567.000000	1569.000000	1570.000000	1570.000000	1563.000000	409.000000	437.000000	499.000000	593.000000	612.000000	746.000000	829.000000	966.000000	179.000000	192.000000	197.000000	200.000000	368.000000	370.000000	386.000000	378.000000	402.000000	412.000000	424.000000	430.000000	434.000000	435.000000	436.000000	436.000000	4288.000000	4366.000000	4967.000000	5044.000000	4936.000000	4857.000000	4741.000000	4686.000000	1049.000000	1824.000000	3461.000000
mean	92.017992	86.390811	77.974030	107.271957	110.104298	73.928335	58.816583	59.642007	164.868145	91.047394	85.353546	92.596227	68.501629	60.761364	61.179336	104.678846	94.423800	75.294163	107.608886	118.502668	81.864221	60.588686	59.961438	75.715618	68.528698	60.194795	60.332129	76.962169	54.648883	56.121925	59.714467	111.509157	103.250748	81.746568	101.160582	106.310234	75.319010	60.498390	58.554497	99.902506	92.626753	72.116685	75.925413	92.390270	69.615453	60.004503	58.384859	59.665230	50.825820	50.237658
std	6.005148	5.572234	8.389878	6.446090	9.415272	7.362007	3.933406	6.841004	14.395855	39.251020	24.425163	16.505927	5.253532	4.789278	7.581061	6.892035	4.123885	3.448186	6.184023	5.252478	3.498104	2.915279	2.681080	7.830648	7.746373	6.569510	5.763363	9.851860	5.391374	6.321088	7.773952	27.356155	25.868541	18.001874	28.357297	20.671382	15.944648	13.746859	12.728755	19.817720	14.544858	6.797960	8.818024	14.604734	9.496760	7.983352	4.964799	2.986022	3.773570	3.695830
min	71.274534	61.611789	50.573090	84.319527	87.988728	53.915681	41.329243	28.469182	125.249880	74.047564	69.603852	74.660346	60.389228	52.059542	43.690718	83.379435	81.050723	66.660673	87.551765	100.408828	70.252272	48.377149	42.338154	53.946476	51.359806	44.469473	43.787464	43.895560	40.793988	36.350701	43.450319	73.250614	66.254367	58.395045	60.035540	58.002171	42.424838	34.917746	34.507602	54.537289	64.068315	55.295119	59.018591	63.240606	43.773042	38.406954	47.364189	49.927987	38.471109	41.306530
2.5%	79.290844	74.528975	63.876227	94.882491	94.949389	63.312943	51.505725	45.610871	150.893603	76.624588	74.536295	81.245414	63.339557	56.316918	53.613932	88.370756	85.970329	68.715172	95.341394	107.319311	73.688511	52.393110	52.678298	59.060405	52.324227	46.210260	47.576138	59.280506	44.501709	40.687911	46.665420	74.091354	68.585434	60.033661	61.755615	71.853762	49.204100	35.473203	35.440768	76.191814	74.133712	63.693213	65.506673	73.900233	58.292767	50.443675	50.657739	52.259418	41.771970	43.556548
25%	88.526639	83.447599	73.198409	102.891764	103.476367	68.846922	56.010668	56.143990	159.963083	81.285597	77.790668	86.510929	65.729020	58.362987	56.056007	101.603689	91.566926	73.214170	102.935325	116.177159	80.610123	59.169902	59.516981	71.755705	63.132385	55.691935	56.457682	71.328454	51.796658	52.512388	54.348788	80.625940	76.057109	64.589622	69.007232	82.451967	59.395270	48.659040	46.578631	81.776810	78.696750	67.415090	68.824927	80.576582	63.582569	55.694614	55.164823	58.272293	48.675114	47.887450
50%	91.630257	86.103130	76.214457	107.022227	108.886657	73.239214	59.331690	59.215096	163.255041	82.804797	79.332946	88.954743	67.027475	59.706667	58.688937	105.497863	94.790453	75.495416	109.051458	119.572237	81.934695	61.372094	60.681659	77.500738	69.637654	60.433441	61.371157	76.334670	55.204096	55.933880	57.310864	112.758798	102.834552	79.442607	102.792563	113.466331	79.654977	61.697934	60.449408	91.734709	89.623317	70.136512	72.538165	87.085898	67.236213	57.699422	57.115360	60.160760	50.692495	50.727470
75%	96.640049	90.556029	82.294617	112.096901	114.976130	77.235285	61.898480	63.347934	165.906664	84.229868	82.340444	91.402491	69.417171	62.070897	63.316588	109.781746	97.608945	77.325865	111.836139	122.245235	83.360243	62.435779	61.347449	80.095639	72.697239	64.499733	64.399260	81.505728	58.226821	60.867060	64.973072	135.822436	129.754458	95.809650	129.957604	119.131628	85.857255	69.880305	70.291425	118.716239	106.429179	75.719462	82.894914	103.521574	75.098884	63.759995	61.268355	61.179078	53.155048	51.937046
97.5%	103.933588	95.583254	97.323221	118.441945	132.925730	89.602914	64.740581	75.239969	207.276980	199.118649	176.967152	151.489954	82.306710	70.843367	82.599720	115.133447	101.320813	85.090230	117.149755	126.313890	86.609769	64.681776	62.560937	88.473722	82.241410	71.411044	68.937072	102.916788	63.650568	68.051423	73.901229	152.518865	139.632716	118.621598	152.911916	143.660622	106.731261	86.165339	81.044174	131.539968	115.334738	85.048185	94.474197	122.003194	91.682238	79.805848	67.431143	65.458598	58.714801	57.496046
max	116.851289	106.351969	120.540637	126.315515	143.808370	111.083014	67.441523	82.950998	426.367353	778.858911	295.869659	292.514372	104.810763	128.198562	104.163372	117.537375	105.779767	87.800296	124.496771	127.472113	101.922449	66.022374	64.060605	113.244225	89.751867	74.783118	78.324788	114.593187	71.453293	73.002232	82.061544	162.037417	145.127694	122.350396	156.028937	144.684377	109.363575	91.640258	86.033215	174.184442	152.744892	137.166419	122.917740	193.165455	132.634029	120.496917	87.835021	68.143543	62.088274	63.711292

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	91.642956	NaN	105.497863	77.502471	112.758798	91.734709	NaN
2011	86.127027	163.342554	94.790453	69.637654	102.834552	89.623317	NaN
2012	76.242205	82.804797	75.495416	60.435458	79.442607	70.136512	NaN
2013	107.023902	79.332946	109.051458	61.371157	102.792563	72.538165	NaN
2014	108.889073	88.995977	119.572237	76.502404	113.466331	87.085898	NaN
2015	73.412060	67.027475	81.934850	55.225698	79.654977	67.237333	60.160760
2016	59.386822	59.710989	61.372094	55.933880	61.697934	57.702296	50.694685
2017	59.750699	58.689548	60.681659	57.310864	60.449408	57.116127	51.386261

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	94.591012	NaN	NaN	74.748461	119.518094	91.013086	NaN
2011	81.541409	147.651843	95.315083	67.586972	102.904595	90.658277	NaN
2012	77.769232	70.934357	75.994444	60.223217	81.283416	71.710441	NaN
2013	100.083051	74.627151	108.962743	60.506492	103.764344	73.816333	NaN
2014	102.343692	81.102552	111.813865	73.019874	108.493946	87.620288	NaN
2015	74.092169	54.074342	77.620474	54.325588	76.626139	64.543321	47.904429
2016	59.067273	55.466713	58.401227	55.127363	59.708533	57.139688	43.235054
2017	58.849745	55.630056	57.996400	57.961796	57.311387	57.362941	50.198177

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	91.780130	NaN	105.663158	77.839414	112.975635	92.040608	NaN
2011	86.265822	171.663151	94.886344	69.931324	103.184464	89.915369	NaN
2012	76.709604	84.879363	75.649929	60.659546	79.641184	70.261575	NaN
2013	107.137153	79.962283	109.387344	61.384973	102.860512	72.659026	NaN
2014	109.336825	89.566856	119.955737	76.380780	113.653407	87.089907	NaN
2015	73.490282	68.630289	82.175311	55.308050	79.783423	67.245947	60.315465
2016	59.958390	60.237333	61.515509	56.104792	61.752049	57.813635	51.169277
2017	61.861851	59.110446	60.935487	57.415140	60.635622	57.190056	51.403162

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	94.457796	NaN	NaN	74.741102	117.636972	91.053349	NaN
2011	80.960072	152.318357	95.362220	67.565627	103.318216	90.694282	NaN
2012	77.364859	71.723508	76.173223	60.026348	81.424795	71.890506	NaN
2013	98.895664	75.228430	108.980108	60.332796	104.213755	73.925827	NaN
2014	101.134964	81.256712	111.983595	72.876636	108.828758	87.539732	NaN
2015	71.753935	54.244745	77.852748	54.124228	76.857902	64.438592	45.296315
2016	58.082556	55.733125	58.163408	54.740153	59.704050	57.255894	41.435422
2017	60.152692	55.827446	58.063913	57.723777	56.878965	57.451400	43.670894

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	91.789179	NaN	105.663158	77.839414	112.975635	92.040608	NaN
2011	86.268224	171.673538	94.886344	69.931324	103.184464	89.961304	NaN
2012	76.715458	84.879363	75.649929	60.661136	79.641184	70.261575	NaN
2013	107.137153	79.962283	109.387344	61.384973	102.860512	72.659026	NaN
2014	109.338862	89.597928	119.955737	76.531748	113.653407	87.089907	NaN
2015	73.586390	68.632155	82.175435	55.308727	79.783423	67.245947	60.315465
2016	60.015474	60.242900	61.515509	56.104792	61.752049	57.819155	51.169286
2017	62.298697	59.110446	60.935487	57.415140	60.635622	57.190056	52.088554

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	94.766971	NaN	NaN	74.868690	119.748813	91.203533	NaN
2011	81.717005	152.839929	95.362220	67.705604	103.378367	91.077774	NaN
2012	78.568522	71.841250	76.173223	60.273039	81.444175	71.916389	NaN
2013	100.368017	75.278542	108.980108	60.526729	104.270211	73.941487	NaN
2014	103.310672	81.294423	111.998438	73.023562	108.864018	87.627690	NaN
2015	75.168976	54.400999	77.988920	54.333688	76.886210	64.577990	48.039159
2016	61.502654	55.926224	58.763805	55.277872	59.843490	57.309070	44.276462
2017	64.697122	56.012118	58.307083	58.139875	57.422286	57.523415	54.225021

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	109.37760	NaN	118.5928	90.77040	129.87750	106.89810	NaN
2011	102.10010	193.97660	104.4916	80.83840	114.73255	102.60605	NaN
2012	90.92750	97.70400	83.0162	70.27660	88.80570	80.90280	NaN
2013	126.72775	95.37130	116.2047	70.30390	112.78160	83.88555	NaN
2014	131.29445	107.04620	124.8249	87.30930	119.94860	97.96785	NaN
2015	88.43770	79.49995	88.4539	63.84860	86.88680	76.16690	72.9487
2016	73.72300	70.06105	67.8385	64.80445	68.98870	66.23390	63.1331
2017	76.93390	69.36600	65.3982	66.44190	68.53490	65.49140	66.4538

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	111.2517	NaN	NaN	86.92090	132.97770	106.03490	NaN
2011	93.1784	173.00860	105.50010	77.71110	115.39015	104.07990	NaN
2012	92.6374	82.76530	84.50340	69.00330	91.31930	82.74450	NaN
2013	116.3315	89.29950	117.86840	69.03940	116.56040	84.98410	NaN
2014	123.2419	97.32225	116.99075	84.53320	113.77750	97.37860	NaN
2015	86.4133	63.83250	85.39750	62.22110	84.56890	72.98540	54.5495
2016	70.2491	64.79050	65.02210	63.61755	67.75440	65.74120	51.1222
2017	70.3169	65.61200	62.86090	66.82590	64.31565	65.82685	50.9204

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	109.38250	NaN	118.5928	90.77040	129.87750	106.89810	NaN
2011	102.11430	194.01830	104.4916	80.83840	114.73255	102.60605	NaN
2012	90.93025	97.70690	83.0162	70.27660	88.80570	80.91480	NaN
2013	126.72775	95.37130	116.2047	70.30390	112.78160	83.88555	NaN
2014	131.30400	107.06850	124.8249	87.31920	119.94860	97.96785	NaN
2015	88.48905	79.50125	88.4555	63.95790	86.88680	76.17050	72.9487
2016	73.80240	70.08585	67.8386	64.80445	68.98870	66.23390	63.1331
2017	77.45070	69.38910	65.3982	66.47765	68.53490	65.49140	67.1264

ISOwecc	CAISO	ERCOT	ISONE	MISO	NYISO	PJM	WECC -CAISO
yearlmp
2010	111.95470	NaN	NaN	87.1449	135.25100	106.37000	NaN
2011	94.63030	173.41900	105.50010	77.9661	115.49405	104.23975	NaN
2012	94.52365	82.94470	84.50340	69.9133	91.36170	82.80290	NaN
2013	118.28190	89.36690	117.86840	69.1420	116.61170	85.04810	NaN
2014	126.15765	97.35920	116.99685	84.8838	113.88280	97.55180	NaN
2015	90.29275	63.96395	85.68730	62.6773	84.87550	73.22020	57.81550
2016	74.94720	64.97755	65.75870	64.0214	67.82570	65.78420	54.39795
2017	76.77800	65.92965	63.19395	67.0154	64.43620	65.90585	66.26690

ISOwecc	CAISO								ERCOT							ISONE								MISO								NYISO								PJM								WECC -CAISO
yearlmp	2010	2011	2012	2013	2014	2015	2016	2017	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2015	2016	2017
count	2114.000000	2121.000000	2150.000000	2205.000000	2234.000000	2236.000000	2237.000000	2209.000000	1560.000000	1563.000000	1567.000000	1570.000000	1572.000000	1570.000000	1564.000000	0.0	437.000000	499.000000	593.000000	612.000000	746.000000	829.000000	966.000000	179.000000	192.000000	197.000000	200.000000	368.000000	370.000000	386.000000	378.000000	402.000000	412.000000	424.000000	430.000000	434.000000	435.000000	436.000000	436.000000	4288.000000	4366.000000	4967.000000	5044.000000	4936.000000	4857.000000	4741.000000	4686.000000	1049.000000	1824.000000	3461.000000
mean	97.383341	81.933103	80.399598	98.337395	101.255145	69.287040	54.830548	53.883991	149.192436	80.347571	79.848675	84.395666	55.213807	56.717095	58.282512	NaN	94.943911	75.989413	106.772965	110.967069	76.995658	57.049205	57.084254	73.260644	67.043250	59.917161	59.637142	73.590786	54.056863	55.260440	59.939343	113.628707	102.967354	82.285210	103.588846	103.571142	73.856640	60.016645	54.867060	100.114408	93.644976	73.500016	75.547609	91.632134	66.804731	58.730709	58.175067	44.451403	40.501281	39.456444
std	10.789130	7.673545	13.325080	6.537813	9.140125	10.637636	4.960570	7.212112	13.388035	44.873751	19.277648	14.512568	4.987191	5.904052	8.911151	NaN	4.537097	3.888703	7.163677	5.116133	2.570858	2.926616	3.077326	7.402907	7.063501	5.861208	6.579268	10.656896	6.660088	6.611914	8.587664	29.692648	24.522276	17.577749	32.072987	21.040395	15.916859	15.165319	12.147695	21.153357	15.321099	6.778444	9.007610	13.667067	9.047200	7.661092	4.619127	3.071947	7.284882	7.350931
min	51.070740	59.563879	45.561790	80.509229	79.236077	42.841082	33.778017	27.826508	129.058755	62.932544	66.772353	72.717154	47.472676	48.831916	35.322076	NaN	79.852823	65.503191	83.752796	94.736047	68.085368	46.584139	36.514438	52.146149	50.671697	40.998470	37.805957	34.903865	37.677918	34.943804	41.593879	73.566140	67.309719	58.707083	56.933855	54.156004	36.163124	31.323744	30.013405	46.328597	63.415392	59.622172	60.144651	59.167099	42.067349	37.751920	45.906879	35.683326	24.534421	16.487856
2.5%	79.962447	65.525861	58.694705	85.801711	88.097041	51.183557	45.594587	40.140121	135.169008	64.812043	68.982385	74.547617	50.434933	52.015167	49.336312	NaN	86.420153	69.091122	92.218752	100.464073	70.150784	48.937462	46.663027	58.478083	53.508832	48.782820	46.243968	55.202733	42.083453	40.656617	46.746613	74.615701	70.260115	60.910039	59.264636	69.096340	46.027522	32.970313	31.845105	75.009021	73.525858	65.161414	64.485819	74.265051	56.345561	48.696140	50.873202	36.744722	27.140630	30.513905
25%	90.474501	77.541906	71.777296	94.168444	95.138763	59.903849	51.726545	49.279463	143.930722	68.742494	72.929978	78.893075	52.787009	54.072354	52.738248	NaN	91.616091	73.153077	101.608385	108.167476	75.569785	55.679191	56.374634	69.660212	62.376573	55.761479	56.535742	67.862806	50.118637	51.272673	54.003958	81.252400	77.330389	65.855771	72.862473	82.694814	59.375192	48.145915	44.711936	80.953705	79.554292	69.155799	67.411408	80.750080	61.330469	54.681888	54.906461	42.654918	35.620734	35.269917
50%	94.208201	80.671016	76.621147	98.699543	99.847372	70.537787	55.657016	52.814589	147.202962	70.834097	74.592091	81.070067	53.928449	55.293798	55.402269	NaN	95.315083	75.994444	108.962743	111.806277	77.476387	57.768955	57.717520	74.620116	67.368666	59.939321	60.310346	72.871556	54.091967	54.724383	57.560303	117.453727	102.822357	81.261827	103.703793	108.451109	76.588783	59.509091	56.736561	90.926518	90.472253	71.671996	73.717128	87.549573	64.386584	57.117897	57.307566	45.203972	40.173297	37.422478
75%	105.650250	87.906132	87.807050	103.679805	104.931149	76.438079	58.533210	58.849791	151.751025	72.567124	78.258475	83.782106	55.470331	57.398486	60.692260	NaN	98.591732	78.148675	112.369067	114.754629	78.879280	59.179083	58.692769	77.112808	70.709486	63.739195	63.406566	78.008657	57.209916	59.418730	65.620191	141.323122	128.564433	94.276219	125.067757	117.235535	87.736983	73.771412	64.923206	120.202757	107.706333	76.492220	82.725102	101.219987	71.138116	62.008205	60.854280	46.234724	44.769346	41.721657
97.5%	117.711876	97.468346	110.509515	108.335215	125.277196	91.561252	62.807131	69.971012	175.099512	193.964647	153.284960	134.583828	70.543404	68.099911	82.103967	NaN	102.214150	87.524096	115.765264	119.509831	80.906720	60.887302	60.655079	86.084977	82.078583	70.693996	72.496147	102.388870	69.023536	68.542420	78.149964	160.141739	151.733667	123.329648	182.228015	154.481630	102.206167	88.307539	72.066345	134.491301	120.000765	87.376019	92.890206	117.639111	89.641035	78.742475	66.709339	49.831682	54.432988	56.307908
max	130.018708	99.718389	120.537862	115.165879	132.967096	110.014158	64.641734	74.620503	326.267108	694.822127	219.394761	197.115729	87.213937	141.959905	128.990140	NaN	108.772527	91.392987	121.951244	120.849700	83.699451	62.485980	62.265714	114.945681	87.924704	82.178646	79.830470	114.276118	70.988370	72.925825	86.511683	160.141739	153.641869	123.838466	204.811975	161.456743	105.433792	98.416609	77.479656	164.998095	142.928318	138.588866	125.289742	187.543788	128.250100	111.255032	80.494622	52.701057	58.409003	60.736481

ISOwecc	CAISO								ERCOT							ISONE								MISO								NYISO								PJM								WECC -CAISO
yearlmp	2010	2011	2012	2013	2014	2015	2016	2017	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2015	2016	2017
count	2118.000000	2121.000000	2150.000000	2204.000000	2234.000000	2236.000000	2237.000000	2209.000000	1559.000000	1563.000000	1567.000000	1569.000000	1570.000000	1570.000000	1563.000000	409.000000	437.000000	499.000000	593.000000	612.000000	746.000000	829.000000	966.000000	179.000000	192.000000	197.000000	200.000000	368.000000	370.000000	386.000000	378.000000	402.000000	412.000000	424.000000	430.000000	434.000000	435.000000	436.000000	436.000000	4288.000000	4366.000000	4967.000000	5044.000000	4936.000000	4857.000000	4741.000000	4686.000000	1049.000000	1824.000000	3461.000000
mean	45.175477	40.237222	36.303819	47.481951	50.994203	34.050025	27.128030	27.708089	79.275421	43.978884	42.241667	46.988858	36.076308	30.178078	30.631365	62.701305	57.080122	43.348422	62.898107	70.691908	46.726974	33.267879	34.606131	43.852098	41.093687	33.099851	35.500079	43.574573	30.953006	30.460430	32.883802	66.534707	62.509990	47.897464	60.056870	64.322450	43.538906	33.636858	34.244757	57.241275	55.410852	40.478193	44.752154	54.405264	40.520470	33.531293	33.451273	30.790701	24.539064	24.305935
std	1.737670	1.901685	3.342362	2.845445	3.864041	3.895063	2.071822	3.342419	5.134065	16.197393	9.472674	6.762032	1.618363	2.503227	4.007041	2.558203	1.662777	1.401016	1.623996	1.745539	1.598961	0.858068	1.091594	4.723929	5.063609	4.158174	4.082144	5.693991	3.200513	3.127798	3.816320	12.151837	12.236261	8.864698	13.502036	9.572891	7.944598	6.599698	6.104059	9.343655	6.604753	3.270237	3.914005	6.981187	4.819349	3.801254	2.141938	1.142863	1.227478	2.260371
min	33.313455	28.391637	29.209682	41.114637	42.012615	25.643363	18.246635	12.993617	60.899811	36.437815	35.801061	40.005112	33.776167	26.829091	21.251504	55.011258	52.674335	40.901957	58.110155	64.704386	43.091132	29.473429	25.390258	33.593195	28.939186	23.871001	25.677652	26.549879	21.980653	21.042636	23.417048	47.526796	44.566424	36.441812	38.790390	39.838320	26.843099	20.533519	21.521889	33.961543	38.811916	30.034810	33.039955	36.890734	24.813719	22.010187	27.745947	27.527130	21.816118	20.040742
2.5%	41.852121	35.982966	31.426159	42.523440	45.338306	28.324880	22.622647	20.772181	72.730362	39.290432	38.860819	44.198561	34.667877	28.212429	26.756687	57.172491	53.600850	41.352215	58.821619	66.606596	43.804245	31.342216	31.511263	34.678524	29.720089	25.084395	26.858685	34.291704	24.681864	23.642875	26.157327	48.546923	45.653807	37.131438	40.261450	47.023664	29.728164	20.964718	22.045894	44.438845	45.299737	36.167194	39.905608	45.778179	34.451172	28.125044	29.741175	29.204024	22.774140	21.489551
25%	44.349947	39.338735	34.138560	45.388035	48.706638	31.444474	25.799738	25.543496	78.034765	40.154849	39.305126	44.922410	35.081047	29.116750	28.318281	60.875639	55.755952	42.607818	62.089156	70.037166	45.943473	32.859338	34.481204	41.402486	38.543199	30.291891	32.139662	39.847108	28.624818	28.051891	30.474410	53.792882	50.284977	40.391129	46.352870	53.856054	36.842006	28.839687	28.851087	48.170289	50.025771	38.252528	41.443186	48.659258	37.921311	32.062366	32.238860	29.941842	23.723351	22.266408
50%	45.325221	40.417082	35.591168	47.221461	50.624526	33.848354	27.562865	27.703784	79.033268	40.427457	39.773405	45.230950	35.470877	29.540111	29.010892	62.599238	56.953720	43.116905	63.002813	70.751555	46.708785	33.328241	34.789771	43.293228	41.132766	33.426375	36.261442	43.106767	31.837814	30.620093	32.132992	66.853404	61.773388	46.395278	60.355995	68.232393	45.406957	33.805654	35.720484	54.546457	54.993100	40.112320	44.514600	52.933508	39.430827	33.158103	33.341268	30.262247	24.467022	24.026835
75%	46.172183	41.439087	37.220480	49.535416	52.423757	35.830691	28.664876	29.722195	80.056644	41.362006	41.296605	46.365705	36.755853	30.359646	31.481967	64.956181	58.375755	43.891171	63.799581	71.642525	47.233286	33.658519	35.030785	46.459535	45.294587	35.552686	38.354987	46.081817	33.204016	32.753133	35.540907	76.797627	75.044306	54.769807	73.577152	69.636073	48.373132	37.519940	39.442471	65.464140	61.176059	42.287993	47.043122	58.898181	42.455278	34.978738	34.818651	31.677827	25.325330	25.506782
97.5%	47.974505	43.070405	44.445137	53.333409	61.901275	42.628439	30.027871	34.382648	90.463622	86.185197	75.644844	67.829329	39.217138	34.887011	42.383883	66.306422	59.685034	49.125540	65.588970	74.614059	49.104235	34.940479	35.830433	51.033141	47.881166	41.380995	42.344131	58.083430	36.204449	35.633473	40.042180	86.088328	80.487189	66.039380	84.777650	81.816996	60.573986	45.744940	45.881320	73.154238	65.827356	46.631942	54.911353	69.185571	52.771575	43.211627	37.085119	32.948971	26.934637	28.577872
max	51.138572	46.800609	54.646675	54.949088	65.550925	51.850500	33.016643	40.043755	180.349037	336.886631	128.588504	137.372822	49.667198	65.213393	52.523915	68.874514	61.840130	49.998567	69.726218	75.338511	56.960907	35.491711	37.047420	70.606498	56.492077	47.209960	46.239649	64.177441	39.863414	39.262600	43.651335	91.530305	83.694936	66.694580	86.942269	84.260185	61.837104	52.523487	47.783136	90.246885	80.060863	71.803614	67.635080	104.543246	72.657013	62.346818	47.560762	35.123601	28.823508	32.996176

ISOwecc	CAISO								ERCOT							ISONE								MISO								NYISO								PJM								WECC -CAISO
yearlmp	2010	2011	2012	2013	2014	2015	2016	2017	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2015	2016	2017
count	2114.000000	2121.000000	2150.000000	2205.000000	2234.000000	2236.000000	2237.000000	2209.000000	1560.000000	1563.000000	1567.000000	1570.000000	1572.000000	1570.000000	1564.000000	0.0	437.000000	499.000000	593.000000	612.000000	746.000000	829.000000	966.000000	179.000000	192.000000	197.000000	200.000000	368.000000	370.000000	386.000000	378.000000	402.000000	412.000000	424.000000	430.000000	434.000000	435.000000	436.000000	436.000000	4288.000000	4366.000000	4967.000000	5044.000000	4936.000000	4857.000000	4741.000000	4686.000000	1049.000000	1824.000000	3461.000000
mean	47.722470	38.134409	37.446310	43.541327	46.956412	31.951283	25.312546	25.040576	71.740774	38.692348	39.547605	42.837528	29.068410	28.177017	29.195855	NaN	57.386932	43.748047	62.382588	66.191907	43.950614	31.321595	32.941855	42.428204	40.199509	32.955593	35.080771	41.631802	30.607480	30.000587	33.011493	67.730433	62.393870	48.218626	61.475648	62.660579	42.712367	33.351253	32.097764	57.335540	56.011095	41.247943	44.579776	53.977953	38.903443	32.836291	33.341369	22.946866	19.382700	18.983464
std	3.596859	2.811932	5.967216	3.046055	4.445673	5.409374	2.661176	3.514949	4.915115	18.765068	7.240725	5.910189	1.807479	3.125806	4.735743	NaN	1.780798	1.710803	2.028008	1.644101	1.059716	0.896241	1.355520	4.457444	4.644408	3.896611	4.356412	5.886771	3.790689	3.379628	4.318758	13.467296	11.431600	8.555320	15.756086	9.894016	8.115088	7.447802	5.922334	10.109035	7.050657	3.153037	4.659081	6.453600	4.735337	3.784676	2.068701	1.481363	2.207097	3.002755
min	25.656461	28.169699	26.050272	35.050690	36.409243	20.373873	14.917904	13.054663	61.666777	30.100248	34.652613	38.758187	26.106244	24.033717	15.524729	NaN	52.621013	40.226043	56.292143	60.382968	40.433814	28.096233	20.804917	33.139887	28.273194	22.007783	21.237074	21.111324	20.956010	20.228213	21.919904	47.654999	45.205316	36.129485	37.071455	37.196611	21.911577	18.304269	18.583957	28.849814	38.416382	32.385147	33.670349	34.514497	23.846809	21.634801	27.615963	18.415059	13.351143	8.307456
2.5%	42.211340	30.901124	29.585655	37.694997	39.833722	22.282715	19.947675	18.265009	65.244088	33.172320	35.885133	39.976350	27.407564	25.625441	24.536075	NaN	53.724158	41.125231	57.056777	62.512804	41.626862	28.765631	28.618913	34.394593	30.194533	26.177683	25.658877	31.605877	23.158561	22.975925	26.198811	49.003968	46.721866	37.285034	37.793285	45.320932	27.897653	19.123702	19.205457	44.156959	44.901593	36.993894	38.811331	45.200059	33.358857	26.960790	29.321262	18.831548	15.640684	14.315065
25%	45.321445	36.592467	33.463885	41.484507	44.123682	27.438398	24.064748	22.333303	69.764634	34.186375	36.751761	40.787187	28.045733	26.966500	26.483221	NaN	56.252230	42.760607	61.429011	65.337710	43.374614	30.990880	32.667344	39.808056	37.217245	29.778996	32.310780	38.856896	27.918146	27.687678	30.060792	53.986528	51.579024	41.027799	47.789293	53.819205	36.518490	28.125172	27.598300	47.751831	50.078733	39.171773	40.540526	49.336735	35.968213	31.318231	32.263453	22.400026	17.719566	16.302861
50%	46.945908	37.881238	35.546166	43.754006	47.074163	33.193210	25.910069	25.011672	71.076056	34.519891	37.518670	41.357884	28.553451	27.542636	27.621548	NaN	57.200568	43.572244	62.770983	66.180022	44.117595	31.430099	33.125203	41.735509	39.981424	33.230227	35.717012	41.062071	30.718069	29.891158	32.137731	67.335810	62.023122	47.427459	61.095318	66.128568	43.297521	32.537043	33.042092	53.880279	56.037231	40.858529	44.325946	53.510857	37.790873	32.603484	33.385809	23.187285	19.214541	19.410569
75%	50.039086	39.797043	40.595816	46.074434	48.857096	35.233354	27.247834	27.819860	72.951362	35.240706	39.186716	42.802924	29.498469	28.307955	30.482552	NaN	58.563235	44.265220	63.753554	67.254875	44.647653	31.853389	33.549094	44.937974	44.127721	35.264507	37.682468	43.880060	32.767407	32.310833	35.918673	80.122308	74.320041	53.893217	71.180449	68.526742	49.640432	39.780411	36.559460	66.113581	62.014214	42.830309	47.240138	57.769963	41.249711	34.501592	34.691266	24.051309	20.592927	20.734315
97.5%	55.642947	44.220885	50.444815	48.792808	58.346928	41.658164	28.627464	31.960051	79.821264	86.487046	67.099756	59.544351	34.354405	33.870302	42.669838	NaN	61.230793	50.535786	65.283695	69.252369	45.833692	32.691725	34.498770	49.908430	48.043689	40.205422	44.314321	57.770145	38.491947	36.741395	42.263403	88.484402	86.594131	68.382460	103.261302	87.979731	57.082776	47.721725	41.424692	75.754769	67.785406	47.885189	55.353135	66.885117	51.498965	42.391175	36.746131	24.892340	23.537740	25.795319
max	56.869385	44.866865	54.572420	51.113866	61.328208	50.599382	33.178101	35.075066	138.007656	300.006977	95.318513	89.325593	48.368216	72.213658	63.937091	NaN	61.772892	51.676310	68.044938	70.148506	48.159494	33.983111	35.346495	71.667337	55.342015	51.878695	46.308729	63.999868	40.116250	39.221507	44.407004	89.056471	87.563832	68.664584	114.125099	91.952168	63.448290	56.407343	43.032461	85.487338	74.915530	72.548234	68.940266	101.926090	70.404004	57.564935	47.245435	25.880763	26.436307	28.116618

ISOwecc	CAISO								ERCOT							ISONE								MISO								NYISO								PJM								WECC -CAISO
yearlmp	2010	2011	2012	2013	2014	2015	2016	2017	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2010	2011	2012	2013	2014	2015	2016	2017	2015	2016	2017
count	2118.000000	2121.000000	2150.000000	2204.000000	2234.000000	2236.000000	2237.000000	2209.000000	1559.000000	1563.000000	1567.000000	1569.000000	1570.000000	1570.000000	1563.000000	409.000000	437.000000	499.000000	593.000000	612.000000	746.000000	829.000000	966.000000	179.000000	192.000000	197.000000	200.000000	368.000000	370.000000	386.000000	378.000000	402.000000	412.000000	424.000000	430.000000	434.000000	435.000000	436.000000	436.000000	4288.000000	4366.000000	4967.000000	5044.000000	4936.000000	4857.000000	4741.000000	4686.000000	1049.000000	1824.000000	3461.000000
mean	1.110444	1.193349	1.148942	1.064677	1.021081	0.977549	0.903693	0.801583	1.597733	1.403138	1.203839	1.151099	1.350195	1.322542	1.249446	1.138182	1.126206	1.118057	1.075804	1.071918	1.090439	1.100763	1.050301	1.220296	1.220006	1.186538	1.146395	1.110429	1.155971	1.179214	1.177995	1.142858	1.152084	1.164686	1.130840	1.068750	1.150780	1.181556	1.120997	1.176081	1.214755	1.168228	1.157981	1.088965	1.166595	1.171990	1.140107	0.972401	0.873984	0.765088
std	0.014350	0.015394	0.041868	0.022447	0.021162	0.050178	0.044231	0.062013	0.037601	0.084605	0.085153	0.055240	0.024463	0.042867	0.054614	0.007917	0.010699	0.011466	0.010408	0.023345	0.022383	0.007103	0.012271	0.025432	0.033600	0.042726	0.034594	0.046720	0.035481	0.029733	0.036575	0.024720	0.035165	0.048053	0.028136	0.034749	0.032840	0.077645	0.044109	0.040093	0.035213	0.018591	0.019657	0.026325	0.028020	0.022072	0.032445	0.035397	0.039732	0.063360
min	1.056745	1.097519	1.035543	1.003922	0.923115	0.806810	0.700139	0.451965	1.323257	1.163643	1.110578	0.948250	1.222508	1.241004	0.860535	1.119896	1.082202	1.091326	1.031159	0.966255	0.984190	1.079167	1.007036	1.171007	1.154172	1.093460	1.047737	0.973476	0.981508	1.120423	1.111534	1.099664	1.103470	1.101220	1.065151	0.884865	1.016456	1.074447	1.041994	0.989044	1.092808	1.122762	1.106164	0.961911	1.062234	1.117059	0.965958	0.883659	0.794868	0.647222
2.5%	1.083454	1.163646	1.095132	1.031357	0.980434	0.879919	0.793333	0.671348	1.531973	1.345093	1.154867	1.118458	1.319398	1.286726	1.192033	1.122241	1.103681	1.105307	1.051325	1.025123	1.054573	1.088616	1.030922	1.177151	1.170519	1.120714	1.103441	1.053315	1.107577	1.133737	1.129260	1.104611	1.105804	1.108591	1.089349	0.986165	1.089643	1.089912	1.061121	1.120089	1.154070	1.140766	1.128262	1.044027	1.115953	1.135154	1.092015	0.922238	0.816979	0.693864
25%	1.100363	1.184714	1.121083	1.049349	1.004867	0.935731	0.871896	0.761790	1.576816	1.365590	1.169742	1.132721	1.334518	1.302276	1.212436	1.132650	1.118994	1.110817	1.072540	1.061366	1.077824	1.097062	1.043044	1.200330	1.194969	1.160643	1.125080	1.079152	1.134398	1.155939	1.151939	1.119568	1.118197	1.142229	1.107144	1.059591	1.134572	1.143814	1.101077	1.151975	1.193487	1.155760	1.144717	1.072390	1.143606	1.157676	1.119698	0.945730	0.849933	0.714267
50%	1.109605	1.193562	1.139462	1.059504	1.022801	0.993296	0.919551	0.816433	1.591313	1.376841	1.182064	1.137982	1.343341	1.311012	1.232272	1.138015	1.128503	1.115548	1.077531	1.069726	1.087579	1.099718	1.048331	1.219608	1.214730	1.175683	1.137146	1.101950	1.146485	1.173928	1.173494	1.152851	1.150326	1.150527	1.133439	1.082699	1.152415	1.157107	1.118062	1.165117	1.207836	1.165354	1.153043	1.086669	1.164359	1.169964	1.137926	0.955955	0.872516	0.754621
75%	1.121073	1.201464	1.159033	1.072330	1.033891	1.007882	0.938259	0.843112	1.615690	1.401108	1.201482	1.151009	1.365563	1.328559	1.271679	1.144296	1.134562	1.123743	1.081940	1.081777	1.102349	1.103239	1.055324	1.235362	1.235564	1.205976	1.157770	1.133950	1.174128	1.200828	1.191547	1.164764	1.183222	1.157951	1.142880	1.086780	1.162718	1.200988	1.132290	1.187071	1.231634	1.176903	1.167761	1.104086	1.192991	1.187087	1.153789	1.007043	0.888963	0.790495
97.5%	1.134649	1.232153	1.255920	1.118123	1.062420	1.061294	0.957645	0.915864	1.684966	1.709042	1.532025	1.294104	1.396932	1.429653	1.389966	1.152741	1.141510	1.163878	1.091152	1.123353	1.131609	1.118150	1.076808	1.274763	1.293309	1.282107	1.221074	1.216974	1.236026	1.237403	1.268782	1.185650	1.195983	1.262294	1.186220	1.109411	1.248784	1.443335	1.256380	1.249406	1.289521	1.212022	1.197563	1.143066	1.217042	1.214015	1.202961	1.035620	0.961668	0.899100
max	1.139584	1.253313	1.367523	1.131657	1.075579	1.085500	1.000511	0.976529	1.779045	1.940491	1.813841	1.664182	1.534029	1.727105	1.507225	1.153784	1.147259	1.173878	1.130427	1.153818	1.202821	1.131670	1.207329	1.304819	1.301418	1.326116	1.253033	1.249215	1.244111	1.266791	1.310353	1.198685	1.263254	1.341058	1.191331	1.131118	1.305214	1.626424	1.405340	1.357076	1.368157	1.272452	1.282994	1.167482	1.302858	1.300367	1.286612	1.045935	0.980711	0.940674